GeneGPS™ Optimizes Expression in Your System.

ATUM has employed a variety of multivariate analysis strategies to identify relationships between gene design variables and protein expression. We have ongoing studies in Bacteria, Yeasts, Mammalian cells and in vivo, Plants, Fungi, Insects, and Cell-free systems. ATUM is using the results of our research to create patented gene design algorithms for numerous host systems. When utilized, these new GeneGPS™ design algorithms routinely produce 10-100 times more protein than competing methods. Results from these investigations can be seen in ATUM PepTalk 2016 Presentation and below.

Bacterial: E. coli

An example, from our work published in PLoS, shows improved expression in a bacterial host system.

Variants expressed in E. coli: Expression of polymerase variants (red squares) and scFv antibody variants (blue diamonds) are shown. Each point shows data from a different codon bias. Genes designed using ATUM’s advanced algorithms are shown in green. Black symbols show the two major algorithms used by our competitors: matching the E. coli genome bias (filled black symbols) or matching the bias found in highly expressed genes (open black symbols).

PLS Model of S. cerevisiae Expression: With this data we are able to correlate expression to codon usage with a predictive PLS model which explains variation of hybrid genes as well as the initial variants.

Mammalian: HEK293 and CHO

Gene design preferences for HEK293 and CHO cell lines. Performance of PLS model of gene sets for 6 different proteins (267 genes) developed on this grant. Variables are restricted to the codon usage frequencies of the genes. Correlation coefficients and significance are indicated for fits to the full data and in cross-validation (prediction of variants when left-out of the training set). Measured values plotted for each protein are normalized to the highest expressing gene variant for that protein.